Artificial Intelligence and Machine Learning

Artificial Intelligence

involves techniques that equip computers to emulate human behavior, enabling them to learn, make decisions, recognize patterns, and solve complex problems in a manner akin to human intelligence

Machine Learning

is a subset of AI, uses advanced algorithms to detect patterns in large data sets, allowing machines to learn and adapt. ML algorithms use supervised or unsupervised learning methods

Deep Learning

a subset of ML which uses neural networks for in-depth data processing and analytical neural networks to extract high-level features from raw input data, simulating the way human brains perceive and understand the world

Generative AI

a subset of DL models that generates content like text, images, or code base on provided input. Trained on vast data sets, these models detect patterns and create outputs without explicit instruction, using a mix of supervised and unsupervised learning

Artificial Intellligence

refers to the simulation of human intelligence in machines

Artificial Intelligence

enables machines to perform tasks such a problem-solving, decision-making, and language understanding

Key Areas of AI

Natural Language Processing (NLP), Computer Vision, Robotics. etc.

Machine Learning

a subset of AI that focuses on teaching computers to learn from data

Machine learning

machines improve their performance on tasks through experience without being explicitly programmed

AI

broader concept involving intelligent systems that cam mimic human behavior

ML

a method used in AI for enabling systems to learn from data

AI

self-driving car

ML

Algorithms predicting traffic or obstacles

Real-World Applications of AI and ML

• Healthcare

• Finance

• Retails

• Autonomous systems

• Image Classification

• Voice User Interfaces

• Posenet and Motion Tracking

• Generative AI

• Deepfakes